Hands-on_Ex03

Author

Liu Chih Yuan

Published

April 29, 2025

Modified

May 3, 2025

Programming Animated Statistical Graphics with R

By using gganimate and plotly r packages, we can create animated data visualization. At the same time, you will also learn how to (i) reshape data by using tidyr package, and (ii) process, wrangle and transform data by using dplyr package.

Concepts

When creating animations, the plot does not actually move. Instead, many individual plots are built and then stitched together as movie frames, just like an old-school flip book or cartoon.

Terminology

Animated graphs are nice, but it does not apply to all use cases. If you are conducting EDA, animated graphs might not be worth the time. But if you are giving a presentation, a few well-placed animated graphs can help audience better understand the topic and figures

Frame: in a animated graph, each frame represents a different point in time or category, when the frame changes the data points on graph are updates as well
Animation Attributes: settings that control how the animation behaves. For example, you can specify the duration of each frame, the easing function used to transition between frames, and whether to start the animation from the current frame or from the beginning.

Loading the packages

readxl makes it easy to get data out from Excel into R
plotly library for plotting interactive statistical graphics
gganimate ggplot extension for creating animated statistical graphs
gifski converts video frames to GIF animations
gapminder An excerpt of the data available at Gapminder.org. We just want to use its country_colors scheme.
tidyverse a set of modern R packages for data science, analytics etc

pacman::p_load( readxl, plotly, gganimate, gifski, gapminder, tidyverse)

Importing Data

col <- c("Country", "Continent")
globalPop <- read_xls("data/GlobalPopulation.xls",
                      sheet="Data") %>%
  mutate_each_(funs(factor(.)), col) %>%
  mutate(Year = as.integer(Year))

Things to learn from the code chunk above

read_xls() of readxl package is used to import the Excel worksheet.
mutate_each_() of dplyr package is used to convert all character data type into factor.
mutate of dplyr package is used to convert data values of Year field into integer.

Unfortunately, mutate_each_() was deprecated in dplyr 0.7.0. and funs() was deprecated in dplyr 0.8.0. In view of this, we will re-write the code by using mutate_at() as shown in the code chunk below.

col <- c("Country", "Continent")
globalPop <- read_xls("data/GlobalPopulation.xls",
                      sheet="Data") %>%
  mutate_at(col, as.factor) %>%
  mutate(Year = as.integer(Year))

note the %>% means “take the result of the previous step and pass to the next function” it is a pipe operator

Instead of using mutate_at(), across() can be used to derive the same outputs

col <- c("Country", "Continent")
globalPop <- read_xls("data/GlobalPopulation.xls",
                      sheet="Data") %>%
  mutate(across(col, as.factor)) %>%
  mutate(Year = as.integer(Year))

gganimate method

gganimate extends the grammar of graphics as implemented by ggplot2 to include the description of animation. It does this by providing a range of new grammar classes that can be added to the plot object in order to customise how it should change with time.

transition_*() defines how the data should be spread out and how it relates to itself across time.
view_*() defines how the positional scales should change along the animation.
shadow_*() defines how data from other points in time should be presented in the given point in time.
enter_*()/exit_*() defines how new data should appear and how old data should disappear during the course of the animation.
ease_aes() defines how different aesthetics should be eased during transitions.

Building static population bubble plot

ggplot(globalPop, aes(x = Old, y = Young, 
                      size = Population, 
                      colour = Country)) +
  geom_point(alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', 
       x = '% Aged', 
       y = '% Young')

Building animated bubble plot

transition_time() of gganimate is used to create transition through distinct states in time (i.e. Year).
ease_aes() is used to control easing of aesthetics. The default is linear. Other methods are: quadratic, cubic, quartic, quintic, sine, circular, exponential, elastic, back, and bounce.

ggplot(globalPop, aes(x = Old, y = Young, 
                      size = Population, 
                      colour = Country)) +
  geom_point(alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', 
       x = '% Aged', 
       y = '% Young') +
  transition_time(Year) +       
  ease_aes('linear')

`ggplotly()` method for animated bubble

gg <- ggplot(globalPop, 
       aes(x = Old, 
           y = Young, 
           size = Population, 
           colour = Country)) +
  geom_point(aes(size = Population,
                 frame = Year),
             alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', 
       y = '% Young')

ggplotly(gg)